






'''simple docstring'''





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_UpperCamelCase						:						Optional[Any]  =					logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')


@dataclass
class  snake_case__      :
					a_        =					field(
					    default="cifar10"     ,				metadata={"help": "Name of a dataset from the datasets package"})
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={"help": "The column name of the images in the files."})
					a_        =					field(default=UpperCamelCase     ,				metadata={"help": "A folder containing the training data."})
					a_        =					field(default=UpperCamelCase     ,				metadata={"help": "A folder containing the validation data."})
					a_        =					field(
					    default=0.15     ,				metadata={"help": "Percent to split off of train for validation."})
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }     ,				)
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }     ,				)




					def 		A      (  self  :		Optional[Any]      )    ->							Tuple:
								UpperCAmelCase_      :       Optional[int]          =       {}
								if self.train_dir is not None:
											UpperCAmelCase_      :       Any          =       self.train_dir
								if self.validation_dir is not None:
											UpperCAmelCase_      :       Tuple          =       self.validation_dir
								UpperCAmelCase_      :       Tuple          =       data_files if data_files else None






@dataclass
class  snake_case__      :
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={
					        "help": (
					            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
					        )
					    }     ,				)
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"})
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={
					        "help": (
					            "Override some existing default config settings when a model is trained from scratch. Example: "
					            "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
					        )
					    }     ,				)
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={"help": "Where do you want to store the pretrained models downloaded from s3"})
					a_        =					field(
					    default="main"     ,				metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}     ,				)
					a_        =					field(default=UpperCamelCase     ,				metadata={"help": "Name or path of preprocessor config."})
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }     ,				)
					a_        =					field(
					    default=0.75     ,				metadata={"help": "The ratio of the number of masked tokens in the input sequence."})
					a_        =					field(
					    default=UpperCamelCase     ,				metadata={"help": "Whether or not to train with normalized pixel values as target."})






@dataclass
class  snake_case__      (					UpperCamelCase):
					a_        =					field(
					    default=1E-3     ,				metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."})






def __UpperCAmelCase   (					A					:			int		)    ->   Optional[int]:
			UpperCAmelCase_      :       Dict          =       torch.stack([example['''pixel_values'''] for example in examples]		)
			return {"pixel_values": pixel_values}
def __UpperCAmelCase   (					)    ->   List[str]:
			# See all possible arguments in src/transformers/training_args.py
			# or by passing the --help flag to this script.
			# We now keep distinct sets of args, for a cleaner separation of concerns.

			UpperCAmelCase_      :       Optional[Any]          =       HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)		)
			if len(sys.argv		) == 2 and sys.argv[1].endswith('''.json'''		):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
			else:
						UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry('''run_mae'''			,       A			,       A		)

			# Setup logging
			logging.basicConfig(
			    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''			,       datefmt='''%m/%d/%Y %H:%M:%S'''			,       handlers=[logging.StreamHandler(sys.stdout		)]			,       )

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			UpperCAmelCase_      :       Optional[Any]          =       training_args.get_process_log_level()
			logger.setLevel(A		)
			transformers.utils.logging.set_verbosity(A		)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
			    + F"distributed training: {bool(training_args.local_rank != -1		)}, 16-bits training: {training_args.fpaa}"		)
			logger.info(F"Training/evaluation parameters {training_args}"		)

			# Detecting last checkpoint.
			UpperCAmelCase_      :       Optional[Any]          =       None
			if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
						UpperCAmelCase_      :       Tuple          =       get_last_checkpoint(training_args.output_dir		)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir		)		) > 0:
									raise ValueError(
									    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
									    '''Use --overwrite_output_dir to overcome.'''		)
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
									    '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''		)

    # Initialize our dataset.
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    data_args.dataset_name			,       data_args.dataset_config_name			,       data_files=data_args.data_files			,       cache_dir=model_args.cache_dir			,       use_auth_token=True if model_args.use_auth_token else None			,       )

			# If we don't have a validation split, split off a percentage of train as validation.
			UpperCAmelCase_      :       Dict          =       None if '''validation''' in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split			,       A		) and data_args.train_val_split > 0.0:
						UpperCAmelCase_      :       Optional[Any]          =       ds['''train'''].train_test_split(data_args.train_val_split		)
						UpperCAmelCase_      :       Tuple          =       split['''train''']
						UpperCAmelCase_      :       Tuple          =       split['''test''']

			# Load pretrained model and image processor
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			UpperCAmelCase_      :       int          =       {
			    '''cache_dir''': model_args.cache_dir,
			    '''revision''': model_args.model_revision,
			    '''use_auth_token''': True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						UpperCAmelCase_      :       Dict          =       ViTMAEConfig.from_pretrained(model_args.config_name			,       **A		)
			elif model_args.model_name_or_path:
						UpperCAmelCase_      :       Optional[int]          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path			,       **A		)
			else:
						UpperCAmelCase_      :       Union[str, Any]          =       ViTMAEConfig()
						logger.warning('''You are instantiating a new config instance from scratch.'''		)
						if model_args.config_overrides is not None:
									logger.info(F"Overriding config: {model_args.config_overrides}"		)
									config.update_from_string(model_args.config_overrides		)
									logger.info(F"New config: {config}"		)

    # adapt config
			config.update(
			    {
			        '''mask_ratio''': model_args.mask_ratio,
			        '''norm_pix_loss''': model_args.norm_pix_loss,
			    }		)

			# create image processor
			if model_args.image_processor_name:
						UpperCAmelCase_      :       Optional[int]          =       ViTImageProcessor.from_pretrained(model_args.image_processor_name			,       **A		)
			elif model_args.model_name_or_path:
						UpperCAmelCase_      :       str          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path			,       **A		)
			else:
						UpperCAmelCase_      :       Optional[int]          =       ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						UpperCAmelCase_      :       List[Any]          =       ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path			,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path		)			,       config=A			,       cache_dir=model_args.cache_dir			,       revision=model_args.model_revision			,       use_auth_token=True if model_args.use_auth_token else None			,       )
			else:
						logger.info('''Training new model from scratch'''		)
						UpperCAmelCase_      :       List[Any]          =       ViTMAEForPreTraining(A		)

			if training_args.do_train:
						UpperCAmelCase_      :       Any          =       ds['''train'''].column_names
			else:
						UpperCAmelCase_      :       Dict          =       ds['''validation'''].column_names

			if data_args.image_column_name is not None:
						UpperCAmelCase_      :       Optional[int]          =       data_args.image_column_name
			elif "image" in column_names:
						UpperCAmelCase_      :       Tuple          =       '''image'''
			elif "img" in column_names:
						UpperCAmelCase_      :       Any          =       '''img'''
			else:
						UpperCAmelCase_      :       List[str]          =       column_names[0]

			# transformations as done in original MAE paper
			# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
			if "shortest_edge" in image_processor.size:
						UpperCAmelCase_      :       Union[str, Any]          =       image_processor.size['''shortest_edge''']
			else:
						UpperCAmelCase_      :       int          =       (image_processor.size['''height'''], image_processor.size['''width'''])
			UpperCAmelCase_      :       Optional[Any]          =       Compose(
			    [
			        Lambda(lambda A		: img.convert('''RGB'''		) if img.mode != "RGB" else img		),
			        RandomResizedCrop(A			,       scale=(0.2, 1.0)			,       interpolation=InterpolationMode.BICUBIC		),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean			,       std=image_processor.image_std		),
			    ]		)

			def preprocess_images(A					:			List[str]		):
						UpperCAmelCase_      :       List[Any]          =       [transforms(A		) for image in examples[image_column_name]]
						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError('''--do_train requires a train dataset'''		)
						if data_args.max_train_samples is not None:
									UpperCAmelCase_      :       Tuple          =       ds['''train'''].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
						# Set the training transforms
						ds["train"].set_transform(A		)

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError('''--do_eval requires a validation dataset'''		)
						if data_args.max_eval_samples is not None:
									UpperCAmelCase_      :       List[Any]          =       (
									    ds['''validation'''].shuffle(seed=training_args.seed		).select(range(data_args.max_eval_samples		)		)
									)
						# Set the validation transforms
						ds["validation"].set_transform(A		)

			# Compute absolute learning rate
			UpperCAmelCase_      :       Tuple          =       (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						UpperCAmelCase_      :       Tuple          =       training_args.base_learning_rate * total_train_batch_size / 2_5_6

			# Initialize our trainer
			UpperCAmelCase_      :       Union[str, Any]          =       Trainer(
			    model=A			,       args=A			,       train_dataset=ds['''train'''] if training_args.do_train else None			,       eval_dataset=ds['''validation'''] if training_args.do_eval else None			,       tokenizer=A			,       data_collator=A			,       )

			# Training
			if training_args.do_train:
						UpperCAmelCase_      :       int          =       None
						if training_args.resume_from_checkpoint is not None:
									UpperCAmelCase_      :       str          =       training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									UpperCAmelCase_      :       Union[str, Any]          =       last_checkpoint
						UpperCAmelCase_      :       Tuple          =       trainer.train(resume_from_checkpoint=A		)
						trainer.save_model()
						trainer.log_metrics('''train'''			,       train_result.metrics		)
						trainer.save_metrics('''train'''			,       train_result.metrics		)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						UpperCAmelCase_      :       List[Any]          =       trainer.evaluate()
						trainer.log_metrics('''eval'''			,       A		)
						trainer.save_metrics('''eval'''			,       A		)

			# Write model card and (optionally) push to hub
			UpperCAmelCase_      :       str          =       {
			    '''tasks''': '''masked-auto-encoding''',
			    '''dataset''': data_args.dataset_name,
			    '''tags''': ['''masked-auto-encoding'''],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**A		)
			else:
						trainer.create_model_card(**A		)
def __UpperCAmelCase   (					A					:			str		)    ->   Tuple:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
						main()










'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


